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McNamara, Danielle S.; Newton, Natalie; Christhilf, Katerina; McCarthy, Kathryn S.; Magliano, Joseph P.; Allen, Laura K. – Grantee Submission, 2023
Analyzing constructed responses, such as think-alouds or self-explanations, can reveal valuable information about readers' comprehension strategies. The current study expands on the extant work by (1) investigating combinations and patterns of comprehension strategies that readers use and (2) examining the extent to which these patterns relate to…
Descriptors: Metacognition, Reading Comprehension, Inferences, Reading Strategies
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Allen, Laura K.; Grasser, Arthur C.; McNamara, Danielle S. – Grantee Submission, 2023
Assessments of natural language can provide vast information about individuals' thoughts and cognitive process, but they often rely on time-intensive human scoring, deterring researchers from collecting these sources of data. Natural language processing (NLP) gives researchers the opportunity to implement automated textual analyses across a…
Descriptors: Psychological Studies, Natural Language Processing, Automation, Research Methodology
Sonia, Allison N.; Joseph, Magliano P.; McCarthy, Kathryn S.; Creer, Sarah D.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2022
The constructed responses individuals generate while reading can provide insights into their coherence-building processes. The current study examined how the cohesion of constructed responses relates to performance on an integrated writing task. Participants (N = 95) completed a multiple document reading task wherein they were prompted to think…
Descriptors: Natural Language Processing, Connected Discourse, Reading Processes, Writing Skills
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software
Öncel, Püren; Flynn, Lauren E.; Sonia, Allison N.; Barker, Kennis E.; Lindsay, Grace C.; McClure, Caleb M.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2021
Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the…
Descriptors: Computer Assisted Instruction, Writing Evaluation, Formative Evaluation, Summative Evaluation
Allen, Laura K.; Creer, Sarah D.; Poulos, Mary Cati – Grantee Submission, 2021
Research in discourse processing has provided us with a strong foundation for understanding the characteristics of text and discourse, as well as their influence on our processing and representation of texts. However, recent advances in computational techniques have allowed researchers to examine discourse processes in new ways. The purpose of the…
Descriptors: Natural Language Processing, Computation, Discourse Analysis, Computer Science
Flynn, Lauren E.; McNamara, Danielle S.; McCarthy, Kathryn S.; Magliano, Joseph P.; Allen, Laura K. – Grantee Submission, 2021
Successful text comprehension requires readers to engage in a number of coherence-building processes. This study examined how analyzing the cohesion of students 'constructed responses can be used to evaluate these coherence-building processes and the extent to which they vary across readers' individual differences and across types of texts. We…
Descriptors: Reading Comprehension, Individual Differences, Protocol Analysis, Literary Genres
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McCarthy, Kathryn S.; Magliano, Joseph P.; Snyder, Jacob O.; Kenney, Elizabeth A.; Newton, Natalie N.; Perret, Cecile A.; Knezevic, Melanie; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2021
The objective in the current paper is to examine the processes of how our research team negotiated meaning using an iterative design approach as we established, developed, and refined a rubric to capture comprehension processes and strategies evident in students' verbal protocols. The overarching project comprises multiple data sets, multiple…
Descriptors: Scoring Rubrics, Interrater Reliability, Design, Learning Processes
McCarthy, Kathryn S.; Allen, Laura K.; Hinze, Scott R. – Grantee Submission, 2020
Open-ended "constructed responses" promote deeper processing of course materials. Further, evaluation of these explanations can yield important information about students' cognition. This study examined how students' constructed responses, generated at different points during learning, relate to their later comprehension outcomes.…
Descriptors: Reading Comprehension, Prediction, Responses, College Students
Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2019
With increased availability of information in modern societies, individuals are often faced with complex decisions regarding how to integrate and judge the veracity of available information. Generally, these issues have been approached using computational techniques to "detect" and "reduce" the spread of information across…
Descriptors: Information Dissemination, Misconceptions, Discourse Analysis, Reader Text Relationship
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McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2019
Writing is a crucial means of communicating with others and thus vital to success and survival in modern society. This article provides an overview of recent research and key findings about writing, including the roles of cognitive and social processes during writing, and educational research on how to improve writing proficiency. Writing…
Descriptors: Writing (Composition), Writing Processes, Writing Research, Educational Research
Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2019
This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…
Descriptors: Language Processing, Science Instruction, Computational Linguistics, Teaching Methods
Crossley, Scott A.; Kim, Minkyung; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2019
Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students' summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However,…
Descriptors: Automation, Writing Evaluation, Natural Language Processing, Artificial Intelligence
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Likens, Aaron D.; McCarthy, Kathryn S.; Allen, Laura K.; McNamara, Danielle D. – Grantee Submission, 2018
Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school…
Descriptors: Reading Comprehension, High School Students, Content Area Reading, Sciences
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